1. Field of the Invention
The present invention relates to a system and method for tuning a process controller using nonlinear tuning rules estimators, including neural networks and fuzzy logic.
2. Description of the Related Art
A Proportional, Integral, Derivative (PID) controller is a common controller used in industrial processes, including computer-controlled industrial processes. Such PID controllers and their variations and combinations, such as P, PI, PD, have enjoyed wide-spread application in the control of industrial processes. Typical industrial processes are controlled by one or more feedback loops incorporating PID controllers.
A fuzzy logic controller (FLC) is also a known process controller used to control process parameters by maintaining process variables within parameters related to desired set point values. FLCs are nonlinear controllers and are becoming more widely used in industrial environments.
One type of known method for parameter tuning of a PID controller is the Ziegler-Nichols method. Relay-oscillation autotuning is also a well-known and recognized tuning technique. Relay-oscillation tuning identifies the Ultimate Gain and Ultimate Period of a process. PID controller settings can be determined from these parameters using Ziegler-Nichols rules and modifications. An extension of the relay-oscillation tuning technique that goes beyond identifying the Ultimate Gain and Ultimate Period is provided in U.S. Pat. No. 5,453,925, “System and Method for Automatically Tuning a Process Controller”, to Wilhelm K. Wojsznis and Terrance L. Blevins, (hereinafter Wojsznis), which is hereby incorporated by this reference, in its entirety, into this patent application.
In recent years, significant progress has been made with model-based tuning and, in particular, with Internal Model Control (IMC) and Lambda tuning. Both approaches result in a first-order closed loop response to setpoint changes. A tuning parameter relating to the speed of response is used to vary the tradeoff between performance and robustness. Both methods adjust the PID controller reset (or reset and rate) to cancel the process pole(s) and adjust the controller Gain to achieve the desired closed-loop response. IMC and Lambda tuning have become popular because oscillation and overshoot are avoided and control performance can be specified in an intuitive way through the closed-loop time constant.
One of the limitations of model-based tuning is the need for process model identification. An equivalent first-order plus Dead Time process model with parameters of Static Gain, Apparent Dead Time, and Apparent Time Constant is usually identified for self-regulating processes. For integrating processes, model parameters of Process Integral Gain and Dead Time are determined. Model identification is typically made by an open-loop step test. Compared to the relay-oscillation method, open-loop methods are not easy to automate. With open-loop methods, human intervention is often required to assure an accurate model due to nonlinearities in the process, valve hysteresis, and load disturbances. A different technique is required for self-regulating and integrating processes.
What is needed is a system and method for tuning in a relay oscillation environment that provides necessary PID tuning parameters over all ranges of model parameters and identifies model parameters of a process.